The AI Governance Audit is a structured, evidence-based assessment that tells you where AI is being used and by whom, what risks that usage creates, and how prepared your leadership, workforce, policies, and controls are to manage it responsibly. It does not tell you to use less AI or more, it gives you a defensible baseline and a prioritized roadmap to a target state you choose deliberately.
Most organizations cannot currently answer basic questions about their own AI use with evidence. They do not know how many tools are in use, which decisions they touch, whether the policies on file are followed, or whether the people accountable for governance are equipped for it. The audit closes that gap before an incident or an inquiry forces the question.
Every maturity score and finding is tied to specific evidence: a document, an interview, a survey, a system record, or an observed practice. Claims that cannot be evidenced are reported as gaps in visibility.
Leadership interviews are tested against workforce surveys. Written policy is tested against observed behavior. Self-reported usage is tested against system signals. Contradictions are among the most valuable outputs.
All seven assessment areas resolve to the same five-level model, so disparate findings can be compared, weighted, and rolled into one defensible index leadership can track over time.
Findings are weighted by likelihood and impact and sequenced so the organization addresses what matters most, first. A one-level gap in a high-risk domain outranks a two-level gap in a low-risk one.
The audit ends with a roadmap to a target state you select deliberately, with sequenced, costed, and owned recommendations, not merely a grade.
Live legal, regulatory, or material exposure is escalated to the engagement sponsor immediately, with recommended containment, rather than held for the final report.
Each assessment area can run on its own, but they are most valuable together because they triangulate. Leadership may report confidence while the workforce reports confusion. Policy may prohibit a practice that Shadow AI proves is widespread. The audit is built to reveal exactly these contradictions.
Where the organization stands today against a defined target state across eight governance domains, and the highest-priority gaps.
Whether the people accountable for AI governance have the literacy, judgment, and alignment to lead it, and whether the organization around them agrees.
Whether the organization can demonstrate, with evidence, that its AI use meets regulatory, contractual, and ethical obligations.
Whether the workforce has the practical competence to use AI effectively, safely, and within policy.
How AI is affecting workforce confidence, trust, and psychological safety, and where that creates adoption or retention risk.
What AI is actually being used outside sanctioned channels, by whom, on what data, and what exposure that creates.
Whether the organization's written governance instruments exist, cohere, comply, and reflect real practice.
Every engagement follows the same four phases, scaled to the size and complexity of the organization. Larger or multi-jurisdiction organizations extend the discovery and analysis phases; a focused single-area assessment compresses to two to three weeks.
Kickoff, scoping interviews, target-state workshop, data-access agreements, communications plan, assessment calendar.
Document review, leadership interviews, workforce surveys, Shadow AI discovery, control walkthroughs, system signal collection.
Scoring against rubrics, triangulation, gap and risk weighting, heat-map construction, validation sessions with stakeholders.
Executive readout, full report, maturity index, prioritized roadmap, optional remediation-support definition.
The deliverable set is designed for two audiences: executives who need the picture in minutes, and the owners who must act on it in detail. Every score and finding is evidenced.
A 30-minute conversation walks through your AI landscape, your concerns, and how a tailored audit would be scoped against them.